Best Practices of OCR Data Capture

Optical character recognition or OCR is one of the best and the fastest methods used for automated data capture. It is one of the most popular technologies used by businesses to convert different forms of data like text and images into machine readable digital data.

This process is highly efficient, as it eliminates the need for manual data entry, thereby minimizing the chances of manual errors. However, it is important to follow a few OCR data capture best practices to make the most out of this method.

5 Best Practices of OCR-based Data Capture

This automated data capture process helps organizations across various industry sectors like manufacturing, financial services, healthcare, etc. to streamline their processes through automation, improve data accuracy, and reduce the costs considerably. OCR has a series of benefits, but the lack of awareness of OCR data capture features, best practices, and OCR process workflow might result in numerous errors. Therefore, businesses need to understand how OCR works and different advantages of using OCR data capture. To make things easy we have listed some of the best practices for OCR-based automated data capture -

Start from the Basics

Data entry teams need to analyze each and every aspect of the data in the non-digital form. Right from the paper quality and language to the layout and font, everything that might affect the quality of the automated data capture must be analyzed. This information will enable data entry team to determine whether the data capture process will be easy or not. Furthermore, some type of documents, such as image-rich documents, may require some special measures to make them OCR compatible or an improvised OCR data capture might be required to successfully digitize such documents.

Define the Project Goals

All the specific goals of the OCR project must be clearly defined. Depending on the project requirements, the data entry team may need to set their objectives along with tentative timelines to complete the data capture process. Post-OCR process the captured images may need further processing based on the accuracy required in the project.

Have a Well Planned Workflow

A streamlined OCR data capture process may not determine whether the project will be a success or a failure. However, the data entry team must ensure that a well-planned OCR process workflow is in place to make sure that the data capture project succeeds as per the expectations.

Perform Quality Check Processes

A successful OCR-based data capture process requires a thorough quality checking (QC) process in place. The QC program will ensure that the project is on track, will be completion on time, and all the objectives will be achieved. Depending on the budget, your data entry team may choose to either go for an entire project review or just a sectional review.

Be Flexible to Variations in Project Scale and Costs

The demands related to the OCR project may vary with time and from project to project. There may be some changes that happen during the project, and the data entry team must be prepared to be flexible and be ready to adapt accordingly. Scale changes may affect the project schedules and pricing and this may require appropriate planning to tackle the changes.

OCR-based Data Capture Tools to Consider

The need for OCR data capture software has become absolutely necessary part of almost all data entry projects. There are thousands of tools available in the market which can be used to convert images of PDFs, documents, transaction statements, invoices, etc. into searchable and digitized documents. The OCR technologies are constantly being upgraded, keeping in mind the trends and changing requirements. Companies need to analyze their requirements and then choose tools that precisely suit their needs. To make things easy for businesses, we have listed some of the top OCR data capture tools which are popularly used in the industry -

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